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Free Energy-Based Methods to Understand Drug Resistance Mutations

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Structural Bioinformatics: Applications in Preclinical Drug Discovery Process

Part of the book series: Challenges and Advances in Computational Chemistry and Physics ((COCH,volume 27))

Abstract

In this chapter, we present an overview of various computational methods, particularly, those that are used to compute the free energy of binding to understand target site mutations that will enable us to foresee mutations that could significantly affect drug binding. We begin by looking at the driving forces that lead to drug resistance and throw some light on the various mechanisms by which drugs can be rendered ineffective. Next, we studied molecular dynamic simulations and its use to understand the thermodynamics of protein–ligand interactions. Building on these fundamentals, we discuss various methods that are available to compute the free energy binding, their mathematical formulations, the practical aspects of each these methods and finally their use in understanding drug resistance.

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References

  1. Gulnik SV, Suvorov LI, Liu B, Yu B, Anderson B, Mitsuya H, Erickson JW (1995) Kinetic characterization and cross-resistance patterns of HIV-1 protease mutants selected under drug pressure. Biochem 34(29):9282–9287

    Article  CAS  Google Scholar 

  2. Schliekelman P, Garner C, Slatkin M (2001) Natural selection and resistance to HIV. Nature 411(6837):545–546

    Article  CAS  PubMed  Google Scholar 

  3. Toprak E, Veres A, Michel J-B, Chait R, Hartl DL, Kishony R (2012) Evolutionary paths to antibiotic resistance under dynamically sustained drug selection. Nature Genet 44(1):101–105

    Article  CAS  Google Scholar 

  4. Yang Z, Nielsen R, Goldman N, Pedersen A-MK (2000) Codon-substitution models for heterogeneous selection pressure at amino acid sites. Genetics 155(1):431–449

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Blanchard JS (1996) Molecular mechanisms of drug resistance in Mycobacterium tuberculosis. Annu Rev Biochem 65(1):215–239

    Article  CAS  PubMed  Google Scholar 

  6. Borst P, Ouellette M (1995) New mechanisms of drug resistance in parasitic protozoa. Annu Rev Microbiol 49(1):427–460

    Article  CAS  PubMed  Google Scholar 

  7. Longley D, Johnston P (2005) Molecular mechanisms of drug resistance. J Pathol 205(2):275–292

    Article  CAS  PubMed  Google Scholar 

  8. Walsh C (2000) Molecular mechanisms that confer antibacterial drug resistance. Nature 406:775–781

    Article  CAS  PubMed  Google Scholar 

  9. Andersson DI, Levin BR (1999) The biological cost of antibiotic resistance. Curr Opin Microbiol 2(5):489–493

    Article  CAS  PubMed  Google Scholar 

  10. Gagneux S, Long CD, Small PM, Van T, Schoolnik GK, Bohannan BJ (2006) The competitive cost of antibiotic resistance in Mycobacterium tuberculosis. Science 312:1944–1946

    Article  CAS  PubMed  Google Scholar 

  11. Böttger EC, Springer B, Pletschette M, Sander P (1998) Fitness of antibiotic-resistant microorganisms and compensatory mutations. Nature Med 4(12):1343–1344

    Article  PubMed  Google Scholar 

  12. Sander P, Springer B, Prammananan T, Sturmfels A, Kappler M, Pletschette M, Böttger EC (2002) Fitness cost of chromosomal drug resistance-conferring mutations. Antimicrob Agents Chemother 46(5):1204–1211

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Cao ZW, Han LY, Zheng CJ, Ji ZL, Chen X, Lin HH, Chen YZ (2005) Computer prediction of drug resistance mutations in proteins. Drug Discov Today 10(7):521–529

    Article  CAS  PubMed  Google Scholar 

  14. Rhee S-Y, Gonzales MJ, Kantor R, Betts BJ, Ravela J, Shafer RW (2003) Human immunodeficiency virus reverse transcriptase and protease sequence database. Nucleic Acids Res 31(1):298–303

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Shafer RW (2006) Rationale and uses of a public HIV drug-resistance database. J Infect Dis 194(Supplement 1):S51–S58

    Article  PubMed  Google Scholar 

  16. Kumar R, Chaudhary K, Gupta S, Singh H, Kumar S, Gautam A, Kapoor P, Raghava GP (2013) CancerDR: cancer drug resistance database. Sci Rep 3:1445

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Sandgren A, Strong M, Muthukrishnan P, Weiner BK, Church GM, Murray MB (2009) Tuberculosis drug resistance mutation database. PLoS Med 6(2):e1000002

    Article  PubMed Central  CAS  Google Scholar 

  18. Carbonell P, Trosset J-Y (2014) Overcoming drug resistance through in silico prediction. Drug Discov Today Technol 11:101–107

    Article  PubMed  Google Scholar 

  19. Hao G-F, Yang G-F, Zhan C-G (2012) Structure-based methods for predicting target mutation-induced drug resistance and rational drug design to overcome the problem. Drug Discov Today 17(19):1121–1126

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Martis EAF, Joseph B, Gupta SP, Coutinho EC, Hdoufane I, Bjij I, Cherqaoui D (2017) Flexibility of important HIV-1 targets and in silico design of anti-HIV drugs. Curr Chem Biol 12(1):23–39

    Article  CAS  Google Scholar 

  21. Chandrika B-R, Subramanian J, Sharma SD (2009) Managing protein flexibility in docking and its applications. Drug Discov Today 14(7):394–400

    Google Scholar 

  22. Coupez B, Lewis R (2006) Docking and scoring-theoretically easy, practically impossible? Curr Med Chem 13(25):2995–3003

    Article  CAS  PubMed  Google Scholar 

  23. Davis IW, Baker D (2009) RosettaLigand docking with full ligand and receptor flexibility. J Mol Biol 385(2):381–392

    Article  CAS  PubMed  Google Scholar 

  24. Lin J-H (2011) Accommodating protein flexibility for structure-based drug design. Curr Top Med Chem 11(2):171–178

    Article  CAS  PubMed  Google Scholar 

  25. Mohan V, Gibbs AC, Cummings MD, Jaeger EP, DesJarlais RL (2005) Docking: successes and challenges. Curr Pharm Des 11(3):323–333

    Article  CAS  PubMed  Google Scholar 

  26. van Gunsteren WF (1988) The role of computer simulation techniques in protein engineering. Protein Eng 2(1):5–13

    Article  PubMed  Google Scholar 

  27. Hansson T, Oostenbrink C, van Gunsteren WF (2002) Molecular dynamics simulations. Curr Opin Struct Biol 12(2):190–196

    Article  CAS  PubMed  Google Scholar 

  28. Binder K, Horbach J, Kob W, Paul W, Varnik F (2004) Molecular dynamics simulations. J Phys Condens Matter 16:S429

    Article  CAS  Google Scholar 

  29. Pissurlenkar RR, Shaikh MS, Iyer RP, Coutinho EC (2009) Molecular mechanics force fields and their applications in drug design. AntiInfect Agents Med Chem 8(2):128–150

    Article  CAS  Google Scholar 

  30. Anderson JA, Lorenz CD, Travesset A (2008) General purpose molecular dynamics simulations fully implemented on graphics processing units. J Comput Phys 227(10):5342–5359

    Article  Google Scholar 

  31. Götz AW, Williamson MJ, Xu D, Poole D, Le Grand S, Walker RC (2012) Routine microsecond molecular dynamics simulations with AMBER on GPUs. 1. generalized born. J Chem Theory Comput 8(5):1542–1555

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Salomon-Ferrer R, Götz AW, Poole D, Le Grand S, Walker RC (2013) Routine microsecond molecular dynamics simulations with AMBER on GPUs. 2. explicit solvent particle mesh Ewald. J Chem Theory Comput 9(9):3878–3888

    Article  CAS  PubMed  Google Scholar 

  33. Beberg AL, Ensign DL, Jayachandran G, Khaliq S, Pande VS (2009) Folding@ home: lessons from eight years of volunteer distributed computing. In: IEEE international symposium on parallel & distributed processing, 2009. IPDPS 2009. IEEE

    Google Scholar 

  34. Larson SM, Snow CD, Shirts M, Pande VS (2009) Folding@ Home and Genome@ Home: Using distributed computing to tackle previously intractable problems in computational biology. DOI: arXiv preprint arXiv:0901.0866

  35. Bruccoleri RE, Karplus M (1990) Conformational sampling using high-temperature molecular dynamics. Biopolymers 29(14):1847–1862

    Article  CAS  PubMed  Google Scholar 

  36. Earl DJ, Deem MW (2005) Parallel tempering: theory, applications, and new perspectives. Phys Chem Chem Phys 7(23):3910–3916

    Article  CAS  PubMed  Google Scholar 

  37. Sugita Y, Okamoto Y (1999) Replica-exchange molecular dynamics method for protein folding. Chem Phys Lett 314(1):141–151

    Article  CAS  Google Scholar 

  38. Huber T, Torda AE, van Gunsteren WF (1994) Local elevation: a method for improving the searching properties of molecular dynamics simulation. J Comput Aided Mol Des 8(6):695–708

    Article  CAS  PubMed  Google Scholar 

  39. Laio A, Parrinello M (2002) Escaping free-energy minima. Proc Natl Acad Sci USA 99(20):12562–12566

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Belubbi AV, Martis EAF (2017) Advanced techniques in bimolecular simulations. In: Bharati SK (ed) Handbook of research on medicinal chemistry, Apple Academic Press (in Press)

    Google Scholar 

  41. Berne BJ, Straub JE (1997) Novel methods of sampling phase space in the simulation of biological systems. Curr Opin Struct Biol 7(2):181–189

    Article  CAS  PubMed  Google Scholar 

  42. Hamelberg D, Mongan J, McCammon JA (2004) Accelerated molecular dynamics: a promising and efficient simulation method for biomolecules. J Chem Phys 120(24):11919–11929

    Article  CAS  PubMed  Google Scholar 

  43. Lei H, Duan Y (2007) Improved sampling methods for molecular simulation. Curr Opin Struct Biol 17(2):187–191

    Article  CAS  PubMed  Google Scholar 

  44. Zuckerman DM (2011) Equilibrium sampling in biomolecular simulation. Annu Rev Biophys 40:41–62

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Böhm HJ, Klebe G (1996) What can we learn from molecular recognition in protein-ligand complexes for the design of new drugs? Angew Chem Int Ed 35(22):2588–2614

    Article  Google Scholar 

  46. Homans S (2007) Dynamics and thermodynamics of ligand–protein interactions. In: Peters T (ed) Bioactive Conformation I. Springer, Berlin, Heidelberg, pp 51–82

    Chapter  Google Scholar 

  47. Whitesides GM, Krishnamurthy VM (2005) Designing ligands to bind proteins. Q Rev Biophys 38(4):385–396

    Article  CAS  PubMed  Google Scholar 

  48. Bronowska, A. K. (2011). Thermodynamics of ligand-protein interactions: implications for molecular design. In: Moreno-Pirajan JC (ed) Thermodynamics—Interaction Studies—Solids, Liquids and Gases. INTECH Open Access Publisher, Croatia, pp 1–48

    Google Scholar 

  49. Datar PA, Khedkar SA, Malde AK, Coutinho EC (2006) Comparative residue interaction analysis (CoRIA): a 3D-QSAR approach to explore the binding contributions of active site residues with ligands. J Comput Aided Mol Des 20(6):343–360

    Article  CAS  PubMed  Google Scholar 

  50. Martis EA, Chandarana RC, Shaikh MS, Ambre PK, D’Souza JS, Iyer KR, Coutinho EC, Nandan SR, Pissurlenkar RR (2015) Quantifying ligand–receptor interactions for gorge-spanning acetylcholinesterase inhibitors for the treatment of Alzheimer’s disease. J Biomol Struct Dyn 33(5):1107–1125

    Article  CAS  PubMed  Google Scholar 

  51. Verma J, Khedkar VM, Prabhu AS, Khedkar SA, Malde AK, Coutinho EC (2008) A comprehensive analysis of the thermodynamic events involved in ligand–receptor binding using CoRIA and its variants. J Comput Aided Mol Des 22(2):91–104

    Article  CAS  PubMed  Google Scholar 

  52. Wang T, Wade RC (2001) Comparative binding energy (COMBINE) analysis of influenza neuraminidase—inhibitor complexes. J Med Chem 44(6):961–971

    Article  CAS  PubMed  Google Scholar 

  53. van Gunsteren WF (1993) Molecular dynamics studies of proteins. Curr Opin Struct Biol 3(2):277–281

    Article  Google Scholar 

  54. Mennucci B (2012) Polarizable continuum model. Wiley Interdisc Rev Comput Mol Sci 2(3):386–404

    Article  CAS  Google Scholar 

  55. Klamt A, Schuurmann G (1993) COSMO: a new approach to dielectric screening in solvents with explicit expressions for the screening energy and its gradient. J Chem Soc Perkin Trans 2(5):799–805

    Article  Google Scholar 

  56. Marenich AV, Cramer CJ, Truhlar DG (2009) Universal solvation model based on solute electron density and on a continuum model of the solvent defined by the bulk dielectric constant and atomic surface tensions. J Phy Chem 113(18):6378–6396

    Article  CAS  Google Scholar 

  57. Hou T, Wang J, Li Y, Wang W (2010) Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. the accuracy of binding free energy calculations based on molecular dynamics simulations. J Chem Inf Model 51(1):69–82

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Hou T, Wang J, Li Y, Wang W (2011) Assessing the performance of the molecular mechanics/Poisson Boltzmann surface area and molecular mechanics/generalized Born surface area methods. II. The accuracy of ranking poses generated from docking. J Comput Chem 32(5):866–877

    Article  CAS  PubMed  Google Scholar 

  59. Sun H, Li Y, Shen M, Tian S, Xu L, Pan P, Guan Y, Hou T (2014) Assessing the performance of MM/PBSA and MM/GBSA methods. 5. improved docking performance using high solute dielectric constant MM/GBSA and MM/PBSA rescoring. Phys Chem Chem Phys 16(40):22035–22045

    Article  CAS  PubMed  Google Scholar 

  60. Sun H, Li Y, Tian S, Xu L, Hou T (2014) Assessing the performance of MM/PBSA and MM/GBSA methods. 4. accuracies of MM/PBSA and MM/GBSA methodologies evaluated by various simulation protocols using PDBbind data set. Phys Chem Chem Phys 16(31):16719–16729

    Article  CAS  PubMed  Google Scholar 

  61. Xu L, Sun H, Li Y, Wang J, Hou T (2013) Assessing the performance of MM/PBSA and MM/GBSA methods. 3. the impact of force fields and ligand charge models. J Phy Chem B 117(28):8408–8421

    Article  CAS  Google Scholar 

  62. Dominy BN, Brooks CL (1999) Development of a generalized born model parametrization for proteins and nucleic acids. J Phy Chem B 103(18):3765–3773

    Article  CAS  Google Scholar 

  63. Jayaram B, Sprous D, Beveridge D (1998) Solvation free energy of biomacromolecules: parameters for a modified generalized born model consistent with the AMBER force field. J Phy Chem B 102(47):9571–9576

    Article  CAS  Google Scholar 

  64. Onufriev A, Bashford D, Case DA (2000) Modification of the generalized Born model suitable for macromolecules. J Phy Chem B 104(15):3712–3720

    Article  CAS  Google Scholar 

  65. Onufriev A, Bashford D, Case DA (2004) Exploring protein native states and large-scale conformational changes with a modified generalized born model. Proteins Struct Funct Bioinf 55(2):383–394

    Article  CAS  Google Scholar 

  66. Homeyer N, Gohlke H (2012) Free energy calculations by the molecular mechanics Poisson—Boltzmann surface area method. Mol Inform 31(2):114–122

    Article  CAS  PubMed  Google Scholar 

  67. Kollman PA, Massova I, Reyes C, Kuhn B, Huo S, Chong L, Lee M, Lee T, Duan Y, Wang W (2000) Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Acc Chem Res 33(12):889–897

    Article  CAS  PubMed  Google Scholar 

  68. Srinivasan J, Cheatham TE, Cieplak P, Kollman PA, Case DA (1998) Continuum solvent studies of the stability of DNA, RNA, and phosphoramidate-DNA helices. J Am Chem Soc 120(37):9401–9409

    Article  CAS  Google Scholar 

  69. Edinger SR, Cortis C, Shenkin PS, Friesner RA (1997) Solvation free energies of peptides: comparison of approximate continuum solvation models with accurate solution of the Poisson-Boltzmann equation. J Phy Chem B 101(7):1190–1197

    Article  CAS  Google Scholar 

  70. Gilson MK, Davis ME, Luty BA, McCammon JA (1993) Computation of electrostatic forces on solvated molecules using the Poisson-Boltzmann equation. J Phy Chem 97(14):3591–3600

    Article  CAS  Google Scholar 

  71. Im W, Beglov D, Roux B (1998) Continuum solvation model: computation of electrostatic forces from numerical solutions to the Poisson-Boltzmann equation. Comput Phys Commun 111(1):59–75

    Article  CAS  Google Scholar 

  72. Baron R, van Gunsteren WF, Hünenberger PH (2006) Estimating the configurational entropy from molecular dynamics simulations: anharmonicity and correlation corrections to the quasi-harmonic approximation. Trends Phys Chem 11:87–122

    CAS  Google Scholar 

  73. Harris S, Laughton C (2007) A simple physical description of DNA dynamics: quasi-harmonic analysis as a route to the configurational entropy. J Phys: Condens Matter 19(7):076103

    CAS  Google Scholar 

  74. Case DA (1994) Normal mode analysis of protein dynamics. Curr Opin Struct Biol 4(2):285–290

    Article  CAS  Google Scholar 

  75. Karplus M, Kushick JN (1981) Method for estimating the configurational entropy of macromolecules. Macromolecules 14(2):325–332

    Article  CAS  Google Scholar 

  76. Tidor B, Karplus M (1993) The contribution of cross-links to protein stability: a normal mode analysis of the configurational entropy of the native state. Proteins Struct Funct Bioinf 15(1):71–79

    Article  CAS  Google Scholar 

  77. Aqvist J, Marelius J (2001) The linear interaction energy method for predicting ligand binding free energies. Comb Chem High Throughput Screen 4(8):613–626

    Article  CAS  PubMed  Google Scholar 

  78. Åqvist J, Medina C, Samuelsson J-E (1994) A new method for predicting binding affinity in computer-aided drug design. Protein Eng 7(3):385–391

    Article  PubMed  Google Scholar 

  79. Hansson T, Marelius J, Åqvist J (1998) Ligand binding affinity prediction by linear interaction energy methods. J Comput Aided Mol Des 12(1):27–35

    Article  CAS  PubMed  Google Scholar 

  80. Åqvist J (1990) Ion-water interaction potentials derived from free energy perturbation simulations. J Phys Chem 94(21):8021–8024

    Article  Google Scholar 

  81. Wang W, Wang J, Kollman PA (1999) What determines the van der waals coefficient β in the LIE (linear interaction energy) method to estimate binding free energies using molecular dynamics simulations? Proteins Struct Funct Bioinf 34(3):395–402

    Article  CAS  Google Scholar 

  82. Bajorath J (2002) Integration of virtual and high-throughput screening. Nat Rev Drug Discov 1(11):882–894

    Article  CAS  PubMed  Google Scholar 

  83. Shoichet BK (2004) Virtual screening of chemical libraries. Nature 432(7019):862–865

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Zwanzig RW (1954) High-temperature equation of state by a perturbation method. i. nonpolar gases. J Chem Phy 22(8):1420–1426

    Article  CAS  Google Scholar 

  85. Zwanzig RW (1955) High-temperature equation of state by a perturbation method. II. polar gases. J Chem Phy 23(10):1915–1922

    Article  CAS  Google Scholar 

  86. van Gunsteren WF (1989) Methods for calculation of free energies and binding constants: successes and problems. In: van Gunsteren WF, Weiner PK (eds) Computer simulation of biomolecular systems: theoretical and experimental applications. Escom, Leiden, pp 27–59

    Google Scholar 

  87. van Gunsteren WF, Berendsen HJ (1987) Thermodynamic cycle integration by computer simulation as a tool for obtaining free energy differences in molecular chemistry. J Comput Aided Mol Des 1(2):171–176

    Article  PubMed  Google Scholar 

  88. Kollman P (1993) Free energy calculations: applications to chemical and biochemical phenomena. Chem Rev 93(7):2395–2417

    Article  CAS  Google Scholar 

  89. Chodera JD, Mobley DL, Shirts MR, Dixon RW, Branson K, Pande VS (2011) Alchemical free energy methods for drug discovery: progress and challenges. Curr Opin Struct Biol 21(2):150–160

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Shirts MR, Mobley DL, Chodera JD (2007) Alchemical free energy calculations: ready for prime time? Annu Rep Comput Chem D A Dixon 3:41–59

    Article  CAS  Google Scholar 

  91. Wang Q, Edupuganti R, Tavares CD, Dalby KN, Ren P (2015) Using docking and alchemical free energy approach to determine the binding mechanism of eEF2K inhibitors and prioritizing the compound synthesis. Front Mol Biosci 2:9

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  92. Massova I, Kollman PA (1999) Computational alanine scanning to probe protein-protein interactions: a novel approach to evaluate binding free energies. J Am Chem Soc 121(36):8133–8143

    Article  CAS  Google Scholar 

  93. Chellappan S, Kairys V, Fernandes MX, Schiffer C, Gilson MK (2007) Evaluation of the substrate envelope hypothesis for inhibitors of HIV-1 protease. Proteins Struct Funct Bioinf 68(2):561–567

    Article  CAS  Google Scholar 

  94. Nalam MN, Ali A, Altman MD, Reddy GKK, Chellappan S, Kairys V, Özen A, Cao H, Gilson MK, Tidor B (2010) Evaluating the substrate-envelope hypothesis: structural analysis of novel HIV-1 protease inhibitors designed to be robust against drug resistance. J Virol 84(10):5368–5378

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Shen Y, Altman MD, Ali A, Nalam MN, Cao H, Rana TM, Schiffer CA, Tidor B (2013) Testing the substrate-envelope hypothesis with designed pairs of compounds. ACS Chem Biol 8(11):2433–2441

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Chellappan S, Kiran Kumar Reddy G, Ali A, Nalam MN, Anjum SG, Cao H, Kairys V, Fernandes MX, Altman MD, Tidor B (2007). Design of mutation‐resistant HIV protease inhibitors with the substrate envelope hypothesis. Chem Biol Drug Des 69(5): 298–313

    Article  CAS  Google Scholar 

  97. Kairys V, Gilson MK, Lather V, Schiffer CA, Fernandes MX (2009) Toward the design of mutation-resistant enzyme inhibitors: further evaluation of the substrate envelope hypothesis. Chem Biol Drug Des 74(3):234–245

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Nalam MN, Ali A, Reddy GKK, Cao H, Anjum SG, Altman MD, Yilmaz NK, Tidor B, Rana TM, Schiffer CA (2013) Substrate envelope-designed potent HIV-1 protease inhibitors to avoid drug resistance. Chem Biol 20(9):1116–1124

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Nalam MN, Schiffer CA (2008) New approaches to HIV protease inhibitor drug design II: testing the substrate envelope hypothesis to avoid drug resistance and discover robust inhibitors. Curr Opin HIV AIDS 3(6):642

    Article  PubMed  PubMed Central  Google Scholar 

  100. Hao G-F, Yang G-F, Zhan C-G (2010) Computational mutation scanning and drug resistance mechanisms of HIV-1 protease inhibitors. J Phy Chem B 114(29):9663–9676

    Article  CAS  Google Scholar 

  101. Tse A, Verkhivker GM (2015) Molecular determinants underlying binding specificities of the ABL kinase inhibitors: combining alanine scanning of binding hot spots with network analysis of residue interactions and coevolution. PLoS ONE 10(6):e0130203

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  102. Hou T, Yu R (2007) Molecular dynamics and free energy studies on the wild-type and double mutant HIV-1 protease complexed with amprenavir and two amprenavir-related inhibitors: mechanism for binding and drug resistance. J Med Chem 50(6):1177–1188

    Article  CAS  PubMed  Google Scholar 

  103. Perryman AL, Lin JH, McCammon JA (2004) HIV-1 protease molecular dynamics of a wild-type and of the V82F/I84V mutant: possible contributions to drug resistance and a potential new target site for drugs. Protein Sci 13(4):1108–1123

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Hou T, McLaughlin WA, Wang W (2008) Evaluating the potency of HIV-1 protease drugs to combat resistance. Proteins: Struct, Funct, Bioinf 71(3):1163–1174

    Article  CAS  Google Scholar 

  105. Wang W, Kollman PA (2001) Computational study of protein specificity: the molecular basis of HIV-1 protease drug resistance. Proc Natl Acad Sci USA 98(26):14937–14942

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Ishikita H, Warshel A (2008) Predicting drug-resistant mutations of HIV protease. Angew Chem Int Ed 47(4):697–700

    Article  CAS  Google Scholar 

  107. Singh N, Frushicheva MP, Warshel A (2012) Validating the vitality strategy for fighting drug resistance. Proteins Struct Funct Bioinf 80(4):1110–1122

    Article  CAS  Google Scholar 

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Acknowledgements

E. A. F. Martis and E. C. Coutinho are grateful to Ian R. Craig, Ph.D. (BASF, Ludwigshafen) for his critical comments and feedback on this chapter. The authors are grateful to Department of Science and Technology (DST), Department of Biotechnology (DBT) and Council of Scientific and Industrial Research (CSIR) for their financial support to build the High-Performance Computing system at the Department of Pharmaceutical Chemistry, Bombay College of Pharmacy. E. A. F. Martis and E. C. Coutinho are also thankful to nVIDIA Corporation for their hardware support grant. E. A. F. Martis is indebted to BASF, Ludwigshafen, Germany for the Ph.D. fellowship and the MCBR4 (2015) consortium (Prof. Dr. P. Comba, University of Heidelberg; Prof. Dr H. Zipse LMU, Munich and Prof. Dr. G. N. Sastry, IICT, Hyderabad for MCBR visiting fellowship to Heine-Heinrich University of Düsseldorf, Germany). E. A. F. Martis would also like to thank Prof. Dr. Holger Gohlke, Heine-Heinrich University of Düsseldorf for his guidance during the sabbatical in his CPCLab. Gratitude is expressed to Sandhya Subash, Ph.D. (Bristol-Meyer-Squibb, India), for her assistance in preparing and proofreading the drafts of this manuscript.

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  1. Molecular Simulations Group, Department of Pharmaceutical Chemistry, Bombay College of Pharmacy, Kalina, Santacruz [E], Mumbai, 400098, India

    Elvis A. F. Martis & Evans C. Coutinho

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Correspondence to Evans C. Coutinho .

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  1. Amrita Centre for Nanosciences and Molecular Medicine, Amrita Institute of Medical Sciences and Research Centre, Kochi, India

    C. Gopi Mohan

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Martis, E.A.F., Coutinho, E.C. (2019). Free Energy-Based Methods to Understand Drug Resistance Mutations. In: Mohan, C. (eds) Structural Bioinformatics: Applications in Preclinical Drug Discovery Process. Challenges and Advances in Computational Chemistry and Physics, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-030-05282-9_1

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